A Novel Periodic Cyclic Sparse Network With Entire Domain Adaptation for Deep Transfer Fault Diagnosis of Rolling Bearing

In recent years, rolling bearing fault diagnosis technique based on deep learning provides a more intelligent and reliable way for the safe operation of mechanical system. However, this technique still exists the problems of high model complexity and poor generalization ability in application. To solve the above problem, a novel periodic cyclic sparse network with entire domain adaptation (PcsNet-EDA) for deep transfer fault diagnosis of rolling bearing is proposed in this paper. The proposed periodic cyclic sparse design pattern makes the weight matrices of the convolutional layer and the fully-connected layer contain a large number of zero-weight parameters with regular arrangement, which effectively reduces the complexity of the model. The proposed EDA simultaneously considers the alignment of both global and local discrepancy, which further enhances the generalization ability of the model. In experimental validation, this paper firstly analyzes the basic diagnostic performance of PcsNet. Then, the transfer diagnostic performance of PcsNet within single-source domain scenarios based on EDA is explored, including the influence of different pre-defined sparse structures on the transfer diagnostic accuracy. The validation shows that the proposed PcsNet and the corresponding transfer model PcsNet-EDA can achieve satisfactory results on different bearing fault datasets for fault diagnosis.